TOWARDS REAL TIME IMAGE DEHAZING ON ANDROID OPERATING SYSTEM

Year 2020, Volume: 62 Issue: 2, 177 - 188, 31.12.2020

Yucel Cımtay

https://doi.org/10.33769/aupse.811810

Cited By: 1

Abstract

Sis, görüntü ve videonun kalitesini düşüren en önemli etkilerden biridir. Bu, kontrastı azaltır ve görsel verimliliği azaltır. Sis giderme için genellikle Atmosferik ışık saçılım modeli (AISM) kullanılır. Bu modelde ölçülecek iki bilinmeyen vardır: atmosferik ışık ve iletim. Bu tür hesaplamalar kolay değildir ve atmosferik ışığın hesaplanması çok zaman alır. Bu durum, gerçek zamanlı olarak sisin çözülmesini zorlaştırır. Sis giderme uygulamaları uzun süredir yaygın olarak uygulanmasına rağmen, bu çalışma android işletim sistemi üzerinde gerçek zamanlı sis gidermnin ilk denemelerinden biridir. Bu, gerçek zamanlı sis gidermeyi bir mobil uygulama veya araca dönüştürmek açısından çok önemlidir.

Keywords

Atmospheric light, Hazy Imagery, Dept Map, Transmission

References

• Wang, W., Yuan, X., Recent advances in image dehazing, IEEE/CAA Journal of Automatica Sinica, 4(3) (2017), 410–436.
• Jia, Z., Wang, H.C., Caballero, R., Xiong, Z.Y., Zhao, J.W., Finn, A., Real-time content adaptive contrast enhancement for see-through fog and rain, Proc. IEEE Int. Conference Acoustics Speech and Signal Processing, (2010), 1378−1381.
• Al-Sammaraie, M.F., Contrast enhancement of roads images with foggy scenes based on histogram equalization, Proc. 10th International Conference on Computer Science & Education, (2015), 95−101.
• Kim, J.H., Sim, J.Y., Kim, C.S., Single image dehazing based on contrast enhancement, Proc. IEEE International Conference Acoustics, Speech and Signal Processing, (2011), 1273−1276.
• Cai, W.T., Liu, Y.X., Li, M.C., Cheng, L., Zhang, C.X., A self-adaptive homomorphic filter method for removing thin cloud, Proc. 19th International Conference Geoinformatics, (2011), 1−4.
• Ilgın, H., Akbulut, A., An Artifact Reduction Method For Block-Based Video Coding, Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 62 (2020 ), 1-13.
• Bülbül, A., Haj Ismai̇l, S., Visually Enhanced Social Media Analysis Of Refugees In Turkey, Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 60 (2018 ), 83-102.
• Gibson, K.B., Belongie, S. J., Nguyen, T. Q., Example based depth from fog, Proc. 20th IEEE International Conference on Image Processing, (2013), 728−732.
• Fang, S., Xia, X. S., Xing, H., Chen, C. W., Image dehazing using polarization effects of objects and airlight, Opt. Express, 22(16) (2014), 19523−19537.
• Galdran, A., Vazquez-Corral, J., Pardo, D., Bertalmio, M., Enhanced variational image dehazing, SIAM Journal of Imaging Science, 8(3) (2015), 1519−154.
• Son, J., Kwon, H., Shim, T., Kim, Y., Ahu, S., Sohng, K., Fusion method of visible and infrared images in foggy environment, Proc. International Conference on Image Processing, Computer Vision, and Pattern Recognition, (2015), 433−437.
• Ancuti, C.O., Ancuti, C., Single image dehazing by multi-scale fusion, IEEE Transaction on Image Processing, 22(8) (2013), 3271−3282.
• Ma, Z. L., Wen, J., Zhang, C., Liu, Q. Y., Yan, D. N., An effective fusion defogging approach for single sea fog image, Neurocomputing, 173 (2016), 1257−1267.
• Guo, F., Tang, J., Cai, Z.X., Fusion strategy for single image dehazing, International Journal of Digital Content Technology and Its Applications, 7(1) (2013), 19−28.
• Zhang, H., Liu, X., Huang, Z.T., Ji, Y.F., Single image dehazing based on fast wavelet transform with weighted image fusion, Proc. IEEE International Conference on Image Processing, (2014), 4542−4546.
• Hao, W., He, M., Ge, H., Wang, C., Qing-Wei G., Retinex-Like Method for Image Enhancement in Poor Visibility Conditions, Procedia Engineering, 15 (2011).
• Kaiming, H., Jian, S., Xiaoou, T., Single Image Haze Removal Using Dark Channel Prior, IEEE Transactions on pattern analysis and machine intelligence, (2011).
• Park, D., Park, H., Han, D. K., Ko, H., Single image dehazing with image entropy and information fidelity, IEEE International Conference on Image Processing (ICIP), (2014), 4037-4041.
• Li, J., Li, G., Fan, H., Image Dehazing Using Residual-Based Deep CNN, IEEE Access, 6 (2018), 26831-26842.
• Li, C., Guo, J., Porikli, F., Fu, H., Pang, Y., A Cascaded Convolutional Neural Network for Single Image Dehazing, IEEE Access, 6 (2018), 24877-24887.
• Haouassi, S., Di, W., Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm, Applied Sciences, (2020).
• Cai, B., Xu, X., Jia, K., Qing, C., Tao, D., DehazeNet: An End-to-End System for Single Image Haze Removal, IEEE Transactions on Image Processing, 25(11) (2016), 5187-5198.
• Rashid, H., Zafar, N., Javed Iqbal, M., Dawood, H., Dawood, H., Single Image Dehazing using CNN, Procedia Computer Science, 147 (2019), 124-130.
• Hassan, H., Bashir, A.K., Ahmad, M. et al., Real-time image dehazing by superpixels segmentation and guidance filter, Journal of Real-Time Image Proc., (2020).
• Yuanyuan, S., Yue. M., Single Image Dehazing on Mobile Device Based on GPU Rendering Technology, Journal of Robotics, Networking and Artificial Life, (2015).
• Lu, J., Dong, C., DSP-based image real-time dehazing optimization for improved dark-channel prior algorithm, Journal of Real-Time Image Processing, (2019).
• C6748 pure DSP device data sheet : Available on: https://www.ti.com/lit/ml/sprt633/ sprt633.pdf?ts=1597690676332&ref_url=https%253A%252F%252Fwww.google.com%252F
• Vazquez-Corral, J., Galdran, A., Cyriac, P. et al., A fast image dehazing method that does not introduce color artifacts, Journal of Real-Time Image Processing, 17 (2020), 607-622.
• Yang, J., Jiang, B., Lv, Z. et al., A real-time image dehazing method considering dark channel and statistics features, Journal of Real-Time Image Processing, 13 (2017), 479-490.
• Diaz-Ramirez, V.H., Hernández-Beltrán, J.E. & Juarez-Salazar, R, Real-time haze removal in monocular images using locally adaptive processing, Journal of Real-Time Image Processing, 16 (2019), 1959–1973.
• Cheng, K., Yu, Y., Zhou, H. et al., GPU fast restoration of non-uniform illumination images, Journal of Real-Time Image Processing, (2020).
• Hernandez-Beltran, J., Diaz-Ramirez, V., Juarez-Salazar, R., Real-time image dehazing using genetic programming, Journal of Optics and Photonics for Information Processing, 13, (2019).
• Fattal, R., Single image dehazing, Proc. of ACM SIGGRAPH, 08 (2008).
• Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M., Lischinski, D., Deep photo: Modelbased photograph enhancement and viewing, ACM Trans. Graph., 27(5) (2008), 1-10.
• He, K., Sun J., Tang, X., Single Image Haze Removal Using Dark Channel Prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12) (2011), 2341-2353.
• Simulink Android Support: Available on: https://www.mathworks.com/hardware-support/android-programming-simulink.html.
• Android Studio. Available on: https://developer.android.com/studio.